Noninvasive hydration monitoring

ABSTRACT

Novel tools and techniques for assessing, predicting and/or estimating effectiveness of hydration of a patient and/or an amount of fluid needed for effective hydration of the patient, in some cases, noninvasively.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present disclosure may be related to the following commonly assignedapplications/patents:

This application claims the benefit, under 35 U.S.C. § 119(e), of thefollowing provisional applications: provisional U.S. Patent ApplicationNo. 61/904,436, filed Nov. 14, 2013 by Mulligan et al. and entitled“Noninvasive Monitoring for Fluid Resuscitation”; and provisional U.S.Patent Application No. 61/905,727, filed Nov. 18, 2013 by Mulligan etal. and entitled “Noninvasive Hydration Monitoring”, both of which areincorporated herein by reference.

This application is also a continuation-in-part of U.S. patentapplication Ser. No. 14/535,171, filed Nov. 6, 2014 by Mulligan et al.and titled, “Noninvasive Predictive and/or Estimative Blood PressureMonitoring (referred to hereinafter as the “'171 application”), which isincorporated herein by reference and which claims the benefit, under 35U.S.C. § 119(e), of provisional U.S. Patent Application No. 61/900,980,filed Nov. 6, 2013 by Mulligan et al. and titled “Noninvasive Predictiveand/or Estimative Blood Pressure Monitoring”, which is incorporatedherein by reference.

This application is also a continuation-in-part of U.S. patentapplication Ser. No. 13/554,483, filed Jul. 20, 2012 by Grudic et al.and titled, “Hemodynamic Reserve Monitor and Hemodialysis Control,(referred to hereinafter as the “'483 application”), which is herebyincorporated by reference and which claims the benefit, under 35 U.S.C.§ 119(e), of provisional U.S. Patent Application No. 61/510,792, filedJul. 22, 2011 by Grudic et al. and entitled “Cardiovascular ReserveMonitor”, and provisional U.S. Patent Application No. 61/614,426, filedMar. 22, 2012 by Grudic et al. and entitled “Hemodynamic Reserve Monitorand Hemodialysis Control”, both of which are hereby incorporated byreference.

The '483 application is also a continuation-in-part of U.S. patentapplication Ser. No. 13/041,006 (the “'006 application”), filed Mar. 4,2011 by Grudic et al. and entitled “Active Physical Perturbations toEnhance Intelligent Medical Monitoring”, which is hereby incorporated byreference, and which claims the benefit, inter alia, of provisional U.S.Patent Application No. 61/310,583, filed Mar. 4, 2010, by Grudic et al.and entitled “Active Physical Perturbations to Enhance IntelligentMedical Monitoring”, which is hereby incorporated by reference. The '006application is a continuation-in-part of U.S. patent application Ser.No. 13/028,140 (the “'140 application,” now U.S. Pat. No. 8,512,260),filed Feb. 15, 2011 by Grudic et al. and entitled “Statistical,Noninvasive Measurement of Intracranial Pressure” which is herebyincorporated by reference, and which claims the benefit of provisionalU.S. Patent Application No. 61/305,110, filed Feb. 16, 2010, by Moultonet al. and titled “A Statistical, Noninvasive Method for MeasuringIntracranial Pressure” which is hereby incorporated by reference.

The '140 application is a continuation in part of InternationalApplication No. PCT/US2009/062119 (the “'119 application”), filed Oct.26, 2009 by Grudic et al. and entitled “Long Term Active Learning fromLarge Continually Changing Data Sets” which is hereby incorporated byreference, and which claims the benefit, under 35 U.S.C. § 119(e), ofprovisional U.S. Patent Application No. 61/252,978 filed Oct. 19, 2009,U.S. Patent Application Nos. 61/166,499, 61/166,486, and 61/166,472,filed Apr. 3, 2009, and U.S. Patent Application No. 61/109,490, filedOct. 29, 2008, each of which is hereby incorporated by reference.

This application may also be related to U.S. patent application Ser. No.14/542,423 filed Nov. 14, 2014, by Mulligan et al. and entitled“Noninvasive Monitoring for Fluid Resuscitation”, which is incorporatedherein by reference.

The respective disclosures of these applications/patents (collectively,the “Related Applications”) are incorporated herein by reference intheir entirety for all purposes.

STATEMENT AS TO RIGHTS TO INVENTIONS MADE UNDER FEDERALLY SPONSOREDRESEARCH OR DEVELOPMENT

This invention was made with government support under grant number0535269 awarded by the National Science Foundation; grant numberFA8650-07-C-7702 awarded by the Air Force Research Laboratory; and grantnumbers W81XWH-09-C-1060 and W81XWH-09-1-0750 awarded by Army MedicalResearch Material and Command. The government has certain rights in theinvention.

COPYRIGHT STATEMENT

A portion of the disclosure of this patent document contains materialthat is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure as it appears in the Patent and TrademarkOffice patent file or records, but otherwise reserves all copyrightrights whatsoever.

FIELD

The present disclosure relates, in general, tools and techniques formedical monitoring, and more particularly, to tools and techniques thatcan monitor, estimate, and/or predict effectiveness of hydration effortsand/or an amount of fluid necessary for effective hydration of apatient, athlete, or other person.

BACKGROUND

Proper Hydration is important in maintaining general fitness. It isespecially important during exercise or excessive environmentalconditions, or in the case of various physical conditions or illness.Further, effective hydration (e.g., neither over nor under hydration) isimportant to optimize physical performance, for example, when engaged insports. Two key questions need to be answered to ensure optimal fluidintake by the body. First, if one is drinking fluids of specific kind,is this fluid leading to effective hydration. If it is not, then eitherthe fluid needs to be changed or the current condition (eitherenvironment or the state of the body) of the individual is preventingproper hydration from taking place. Second, how much fluid should oneconsume in order to prevent over or under hydration?

Unfortunately, there is currently no simple way for the average personto answer these questions quickly and easily. In many cases, by the timea person becomes overtly symptomatic (e.g., headache, dry mouth, lack ofperspiration), that person has already entered a state of substantialdehydration. While invasive tests can be performed in a clinicalsetting, such tests are infeasible in the field, for example, in themidst of an athletic contest. Anecdotal measures for how much to drinkto stay hydrated are imprecise at best, and are equally likely to leadto under hydration or over hydration.

Hence, there is a need for an automated, noninvasive device for earlydiagnosis, real-time monitoring and tracking of hydration effectiveness,for general health and wellness, to avoid complications in the case ofillness or other physical conditions, and to help optimize athleticperformance.

BRIEF SUMMARY

Various embodiments can assess the effectiveness of fluid intakehydration, where effectiveness can be defined, but not limited to, asleading to a better hydration state or maintain an optimal hydrationstate. In one aspect, optimal hydration might be defined as a fluidstate that maximized some performance index/measure, perhaps indicatedby the patient's compensatory reserve index (“CRI,” also referred toherein and in the Related Applications as “cardiac reserve index” or“hemodynamic reserve index” (“HDRI”), all of which should be consideredsynonymous for purposes of this disclosure). (While the term, “patient,”is used herein for convenience, that descriptor should not be consideredlimiting, because various embodiments can be employed both in a clinicalsetting and outside any clinical setting, such as by an athlete before,during, or after an athletic contest or training, a person during dailyactivities, a soldier on the battlefield, etc. Thus, the term,“patient,” as used herein, should be interpreted broadly and should beconsidered to be synonymous with “person.”) In other cases, theassessments might be based on raw waveform data (e.g., PPG waveformdata) captured by a sensor on the patent (such as the sensors describedbelow and the Related Applications, for example). In further cases, acombination of waveform data and calculated/estimated CRI can be used tocalculate the effectiveness of hydration and/or the amount of fluidneeded for effective hydration. In other aspects, such functionality canbe provided by and/or integrated with systems, devices (such as acardiac reserve monitor and/or wrist-worn sensor device), tools,techniques, methods, and software described below and in the RelatedApplications.

The tools provided by various embodiments include, without limitation,methods, systems, and/or software products. Merely by way of example, amethod might comprise one or more procedures, any or all of which areexecuted by a computer system. Correspondingly, an embodiment mightprovide a computer system configured with instructions to perform one ormore procedures in accordance with methods provided by various otherembodiments. Similarly, a computer program might comprise a set ofinstructions that are executable by a computer system (and/or aprocessor therein) to perform such operations. In many cases, suchsoftware programs are encoded on physical, tangible and/ornon-transitory computer readable media (such as, to name but a fewexamples, optical media, magnetic media, and/or the like).

For example, one set of embodiments provides methods. An exemplarymethod might comprise monitoring, with one or more sensors,physiological data of a patient. The method might further compriseanalyzing, with a computer system, the physiological data. Manydifferent types of physiological data can be monitored and/or analyzedby various embodiments, including without limitation, blood pressurewaveform data, plethysmograph waveform data, photoplethysmograph (“PPG”)waveform data (such as that generated by a pulse oximeter), and/or thelike. In an aspect of some embodiments, analyzing the physiological datamight comprise analyzing the data against a pre-existing model. In somecases, the method can further comprise assessing the effectiveness ofhydration efforts, and/or displaying (e.g., on a display device) anassessment of the effectiveness of the hydration efforts. Such anassessment can include, without limitation, an estimate of theeffectiveness at a current time, a prediction of the effectiveness atsome point in the future, an estimate and/or prediction of a volume offluid necessary for effective hydration, an estimate of the probabilitya patient requires fluids, etc.

An apparatus, in accordance with yet another set of embodiments, mightcomprise a computer readable medium having encoded thereon a set ofinstructions executable by one or more computers to perform one or moreoperations. In some embodiments, the set of instructions might compriseinstructions for performing some or all of the operations of methodsprovided by certain embodiments.

A system, in accordance with yet another set of embodiments, mightcomprise one or more processors and a computer readable medium incommunication with the one or more processors. The computer readablemedium might have encoded thereon a set of instructions executable bythe computer system to perform one or more operations, such as the setof instructions described above, to name one example. In someembodiments, the system might further comprise one or more sensorsand/or a therapeutic device, either or both of which might be incommunication with the processor and/or might be controlled by theprocessor. Such sensors can include, but are not limited to, a bloodpressure sensor, an intracranial pressure monitor, a central venouspressure monitoring catheter, an arterial catheter, anelectroencephalograph, a cardiac monitor, a transcranial Doppler sensor,a transthoracic impedance plethysmograph, a pulse oximeter, a nearinfrared spectrometer, a ventilator, an accelerometer, anelectrooculogram, a transcutaneous glucometer, an electrolyte sensor,and/or an electronic stethoscope.

BRIEF DESCRIPTION OF THE DRAWINGS

A further understanding of the nature and advantages of particularembodiments may be realized by reference to the remaining portions ofthe specification and the drawings, in which like reference numerals areused to refer to similar components. In some instances, a sub-label isassociated with a reference numeral to denote one of multiple similarcomponents. When reference is made to a reference numeral withoutspecification to an existing sub-label, it is intended to refer to allsuch multiple similar components.

FIG. 1A is a schematic diagram illustrating a system for estimatingcompensatory reserve, in accordance with various embodiments.

FIG. 1B is a schematic diagram illustrating a sensor system that can beworn on a patient's body, in accordance with various embodiments.

FIG. 2A is a process flow diagram illustrating a method of assessingeffectiveness of hydration, in accordance with various embodiments.

FIG. 2B illustrates a technique for assessing effectiveness ofhydration, in accordance with various embodiments.

FIG. 3A is a process flow diagram illustrating a method estimating apatient's compensatory reserve and/or dehydration state, in accordancewith various embodiments.

FIG. 3B illustrates a technique for estimating and/or predicting apatient's compensatory reserve index, in accordance with variousembodiments.

FIG. 4 is a process flow diagram illustrating a method of generating amodel of a physiological state, in accordance with various embodiments.

FIG. 5 is a generalized schematic diagram illustrating a computersystem, in accordance with various embodiments.

FIGS. 6-8 are exemplary screen captures illustrating display features ofa compensatory reserve monitor showing assessments of hydrationeffectiveness, in accordance with various techniques

DETAILED DESCRIPTION OF CERTAIN EMBODIMENTS

The following disclosure illustrates a few exemplary embodiments infurther detail to enable one of skill in the art to practice suchembodiments. The described examples are provided for illustrativepurposes and are not intended to limit the scope of the invention.

In the following description, for the purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the described embodiments. It will be apparent to oneskilled in the art, however, that other embodiments of the present maybe practiced without some of these specific details. In other instances,certain structures and devices are shown in block diagram form. Severalembodiments are described herein, and while various features areascribed to different embodiments, it should be appreciated that thefeatures described with respect to one embodiment may be incorporatedwith other embodiments as well. By the same token, however, no singlefeature or features of any described embodiment should be consideredessential to every embodiment of the invention, as other embodiments ofthe invention may omit such features.

Unless otherwise indicated, all numbers used herein to expressquantities, dimensions, and so forth should be understood as beingmodified in all instances by the term “about.” In this application, theuse of the singular includes the plural unless specifically statedotherwise, and use of the terms “and” and “or” means “and/or” unlessotherwise indicated. Moreover, the use of the term “including,” as wellas other forms, such as “includes” and “included,” should be considerednon-exclusive. Also, terms such as “element” or “component” encompassboth elements and components comprising one unit and elements andcomponents that comprise more than one unit, unless specifically statedotherwise.

Overview

A set of embodiments provides methods, systems, and software that can beused, in many cases noninvasively, to quickly and accurately assess theeffectiveness of hydration of a patient. Such an assessment can include,without limitation, an estimate of the effectiveness at a current time,a prediction of the effectiveness at some point in the future, anestimate and/or prediction of a volume of fluid necessary for effectivehydration, an estimate of the probability a patient requires fluids,etc. In a particular set of embodiments, a device, which can be worn onthe patient's body, can include one or more sensors that monitor apatient's physiological parameters. The device (or a computer incommunication with the device) can analyze the data captured by thesensors and compare such data with a model (which can be generated inaccordance with other embodiments) to assess the effectiveness ofhydration, as described in further detail below.

Different embodiments can measure a number of different physiologicalparameters from the patient, and the analysis of those parameters canvary according to which parameters are measured (and which, according tothe generated model, are found to be most predictive of theeffectiveness of hydration, including the probability of the need forhydration and/or the volume of fluids needed). In some cases, theparameters themselves (e.g., continuous waveform data captured by aphotoplethysmograph) can be analyzed against the model to makeassessments of hydration effectiveness. In other cases, physiologicalparameters can be derived from the captured data, and these parameterscan be used Merely by way of example, as described further below and the'483 application (already incorporated by reference), directphysiological data (captured by sensors) can be used to estimate a valueof CRI, and this value of CRI can be used to assess the effectiveness ofhydration. In yet other cases, the derived CRI values and raw sensordata can be used together to perform such an assessment.

For example, the '483 application describes a compensatory reservemonitor (also described as a cardiac reserve monitor or hemodynamicreserve monitor) that is able to estimate the compensatory reserve of apatient. In an aspect, this monitor quickly, accurately and/or inreal-time can determine the probability of whether a patient isbleeding. In another aspect, the device can simultaneously monitor thepatient's compensatory reserve by tracking the patient's CRI, toappropriately and effectively guide hydration and ongoing patient care.The same device (or a similar device) can also include advancedfunctionality to assess the effectiveness of hydration, based on themonitored CRI values, as explained in further detail below.

CRI is a hemodynamic parameter that is indicative of theindividual-specific proportion of intravascular fluid reserve remainingbefore the onset of hemodynamic decompensation. CRI has values thatrange from 1 to 0, where values near 1 are associated with normovolemia(normal circulatory volume) and values near 0 are associated with theindividual specific circulatory volume at which hemodynamicdecompensation occurs.

The mathematical formula of CRI, at some time “t” is given by thefollowing equation:

$\begin{matrix}{{{CRI}(t)} = {1 - \frac{{BLV}(t)}{{BLV}_{HDD}}}} & \left( {{Eq}.\mspace{14mu} 1} \right)\end{matrix}$

Where BLV(t) is the intravascular volume loss (“BLV,” also referred toas “blood loss volume” in the Related Applications) of a person at time“t,” and BLV_(HDD) is the intravascular volume loss of a person whenthey enter hemodynamic decompensation (“HDD”). Hemodynamicdecompensation is generally defined as occurring when the systolic bloodpressure falls below 70 mmHg. This level of intravascular volume loss isindividual specific and will vary from subject to subject.

Lower body negative pressure (LBNP) in some linear or nonlinearrelationship λ with intravascular volume loss:BLV=λ·LBNP  (Eq. 2)

can be used in order to estimate the CRI for an individual undergoing aLBNP experiment as follows:

$\begin{matrix}\begin{matrix}{{CRI} = {1 - \frac{{BLV}(t)}{{BLV}_{HDD}}}} \\{\approx {1 - \frac{\lambda \cdot {{LBNP}(t)}}{\lambda \cdot {LBNP}_{HDD}}}} \\{= {1 - \frac{{LBNP}(t)}{{LBNP}_{HDD}}}}\end{matrix} & \left( {{Eq}.\mspace{14mu} 3} \right)\end{matrix}$

Where LBNP(t) is the LBNP level that the individual is experiencing attime “t”, and, LBNP_(HDD) is the LNPB level that the individual willenter hemodynamic decompensation.

Using either CRI data, raw (or otherwise processed) sensor data, orboth, various embodiments can assess the effectiveness of hydration. Inone embodiment, the effectiveness of hydration (“HE”) can be expressedas a value between 0 and 1; when HE=1, hydration is proceedingeffectively, when HE=0, hydration is not producing the desired result,perhaps due to ongoing bleeding. (Of course, other embodiments can scalethe value of HE differently). In an aspect of some embodiments, ageneral expression for the estimate of as follows:HE=ƒ _(HE)(CRI_(t),FV_(t) ,S _(t))  (Eq. 4)

Where HE is a measure of hydration effectiveness, ƒ_(HE)(CRI_(t),FV_(t),S_(t)) is an algorithm embodied by a model generatedempirically, e.g., using the techniques described with respect to FIG. 4below, and/or in the Related Applications, CRI_(t) is a time history ofCRI values (which can range from a singe CRI value to many hours of CRIvalues), FV_(t) is a time history of fluid volume being given to thepatient (which can range from a single value to many hours of values),and S_(t) is a time history of raw sensor values, such as physiologicaldata measured by the sensors, as described elsewhere herein (which canrange from one value to many hours of values).

The functional form of Eq. 4 is similar to but not limited to the formof the CRI model in the sense that time histories of(CRI_(t),FV_(t),S_(t)) data gathered from human subjects at variouslevels of HE are compared to time histories of (CRI_(t),FV_(t),S_(t))for the current patient being monitored. The estimated HE for thecurrent patient is then that which is the closest in(CRI_(t),FV_(t),S_(t)) space to the previously gathered data.

While Eq. 4 is the general expression for HE, various embodiments mightuse subsets of the parameters considered in Eq. 4. For instance, in oneembodiment, a model might consider only the volume of fluid and CRIdata, without accounting for raw sensor input. In that case, HE can becalculated as follows:HE=ƒ _(HE)(CRI_(t),FV_(t))  (Eq. 5)

Similarly, some models might estimate HE based on sensor data, ratherthan first estimating CRI, in which case, HE can be expressed thusly:HE=ƒ _(HE)(FV_(t) ,S _(t))  (Eq. 6)

The choice of parameters to use in modeling HE is discretionary, and itcan depend on what parameters are shown (e.g., using the techniques ofFIG. 4, below) to result in the best prediction of HE.

In another aspect, the effectiveness of hydration can be assessed byestimating or predicting the volume, V, of fluid necessary for effectivehydration of the patient. This volume, V, can indicate a volume of fluidneeded for full hydration if therapy has not yet begun, and/or it canindicate a volume remaining for fully effective hydration if therapy isunderway. Like HE, the value of V can be estimated/predicted using themodeling techniques described herein and in the Related Applications. Ina general case, V can be expressed as the following:V=ƒ _(V)(CRI_(t),FV_(t) ,S _(t))  (Eq. 7)where V is an estimated volume of fluid needed by a patient need toprevent over or under hydration, ƒ_(V)(CRI_(t),FV_(t),S_(t)) is analgorithm embodied by a model generated empirically, e.g., using thetechniques described with respect to FIG. 4 below, and/or in the RelatedApplications, CRI_(t) is a time history of CRI values, FV_(t) is a timehistory of fluid volume being given to the patient, and S_(t) is a timehistory of physiological data received from the one or more sensors.

As with the estimate of HE, various embodiments can employ subsets ofthe parameters used in the general expression of Eq. 7. Thus, differentembodiments might calculate V as follows:V=ƒ _(V)(CRI_(t),FV_(t))  (Eq. 8)orV=ƒ _(V)(FV_(t) ,S _(t))  (Eq. 9)

Yet another way of assessing effectiveness of hydration (which can eveninclude assessing the need for hydration) is estimating the probabilityP_(f) that the patient requires fluids; this probability can estimatethe likelihood that the patient requires hydration if therapy has notbeen initiated, and/or, if hydration therapy is underway, theprobability can estimate the likelihood that further hydration isnecessary. The value of this probability, which can be expressed, e.g.,as a percentage, as a decimal value between 0 and 1, etc. can beestimated using the following expression:P _(ƒ)=ƒ_(P) _(ƒ) (CRI_(t) ,S _(t))  (Eq. 10)where P_(ƒ) is the estimated probability that the patient requiresfluid, ƒ_(P) _(ƒ) (CRI_(t),S_(t)) is a relationship derived based onempirical study, CRI_(t) is a time history of CRI values, and S_(t) is atime history of physiological data received from the one or moresensors. Once again, this general expression can be employed, in variousembodiments, using subsets of the parameters in the general expression,such as the following:P _(ƒ)=ƒ_(P) _(ƒ) (CRI_(t))  (Eq. 11)orP _(ƒ)=ƒ_(P) _(ƒ) (S _(t))  (Eq. 12)

In the estimate of any of HE, V, or P_(f), the function ƒ expresses arelationship that is derived based on empirical study. In a set ofembodiments, for example, various sensor data can be collected from testsubjects before, during, and/or after hydration efforts, duringhemorrhaging, or under other conditions that might simulate suchsituations. This sensor data can be analyzed to develop models, usingtechniques similar to those of FIG. 4 below, which can then be used toestimate various assessments of hydration effectiveness, using, e.g.,the methods described below with respect to FIGS. 2 and 3.

A measure of CRI, HE, V, and/or P_(f) can be useful in a variety ofclinical settings, including but not limited to: 1) acute blood lossvolume due to injury or surgery; 2) acute circulatory volume loss due tohemodialysis (also called intradialytic hypotension); and 3) acutecirculatory volume loss due to various causes of dehydration (e.g.reduced fluid intake, vomiting, dehydration, etc.). A change in CRI canalso herald other conditions, including without limitation changes inblood pressure, general fatigue, overheating and certain types ofillnesses. Accordingly, the tools and techniques for estimating and/orpredicting CRI can have a variety of applications in a clinical setting,including without limitation diagnosing such conditions.

Moreover, measures of CRI, HE, V, and/or P_(f) can have applicabilityoutside the clinical setting. For example, an athlete can be monitored(e.g., using a wrist-wearable hydration monitor) before, during, orafter competition or training to ensure optimal performance (and overallhealth and recovery). In other situations, a person concerned aboutoverall wellbeing can employ a similar hydration monitor to ensure thathe or she is getting enough (but not too much) fluid, ill infants oradults can be monitored while ill to ensure that symptoms (e.g.,vomiting, diarrhea) do not result in dehydration, and the like.Similarly, soldiers in the field (particularly in harsh conditions) canbe monitored to ensure optimal operational readiness.

In various embodiments, a hydration monitor, compensatory reservemonitor, a wrist-wearable sensor device, and/or another integratedsystem can include, but is not limited to, some or all of the followingfunctionality, as described in further detail herein and in the RelatedApplications:

A. Estimating and/or displaying intravascular volume loss to hemodynamicdecompensation (or cardiovascular collapse).

B. Estimating, predicting and/or displaying a patient's compensatoryreserve as an index that is proportional to an approximate measure ofintravascular volume loss to CV collapse, recognizing that each patienthas a unique reserve capacity.

C. Estimating, predicting and/or displaying a patient's compensatoryreserve as an index with a normative value at euvolemia (for example,CRI=1), representing a state in which the patient is normovolemic; aminimum value (for example, CRI=0) which implies no circulatory reserveand that the patient is experiencing CV collapse; and/or an excess value(for example, CRI>1) representing a state in which the patient ishypervolemic; the patient's normalized compensatory reserve can bedisplayed on a continuum between the minimum and maximum values (perhapslabeled by different symbols and/or colors depending on where thepatient falls on the continuum).

D. Determining and/or displaying a probability that bleeding orintravascular volume loss has occurred.

E. Displaying an indicator that intravascular volume loss has occurredand/or is ongoing; as well as other measures of reserve, such as trendlines.

E. Estimating a patient's current blood pressure and/or predicting apatient's future blood pressure.

F. Estimating the current effectiveness of fluid resuscitation efforts.

G. Predicting the future effectiveness of fluid resuscitation efforts.

H. Estimating and/or predicting a volume of fluid necessary foreffective resuscitation.

I. Estimating a probability that a patient needs fluids.

J. Estimating a hydration state of a patient or user.

K. Predicting a future hydration state of a patient or user.

L. Estimate and/or predicting a volume of fluid intake necessary foradequate hydration of a patient or user.

M. Estimating a probability that a patient is dehydrated.

In various embodiments, CRI, HE, V, and/or P_(f) estimates can be (i)based on a fixed time history of patient monitoring (for example a 30second or 30 heart beat window); (ii) based on a dynamic time history ofpatient monitoring (for example monitoring for 200 minutes, the systemmay use all sensor information gathered during that time to refine andimprove CRI estimates, hydration effectiveness assessments, etc.); (iii)based on either establishing baseline estimates when the patient isnormovolemic (no volume loss has occurred); and/or (iv) based on NObaselines estimates when patient is normovolemic.

Certain embodiments can also recommend treatment options, based on theanalysis of the patient's condition (including the estimated/predictedblood pressure, probability of bleeding, state of dehydration, and/orthe patient's estimated and/or predicted CRI). Treatment options caninclude, without limitation, such things as optimizing hemodynamics,ventilator adjustments, IV fluid adjustments (e.g., controlling the flowrate of an IV pump or the drip rate of an IV drip), transfusion of bloodor blood products, infusion of volume expanders, medication changes,changes in patient position and surgical therapy.

As one example, certain embodiments can be used to control an IV drip,IV pump, or rapid infuser. For instance, an embodiment might estimatethe probability that a patient requires fluids and activate such adevice in response to that estimate (or instruct a clinician to attachsuch a device to the patient and activate the device). The system mightthen monitor the progress of the hydration effort (through continual orperiodic assessment of the effectiveness of hydration) andincrease/decrease drip or flow rates accordingly.

As another example, certain embodiments can be used as an input for ahemodialysis procedure. For example, certain embodiments can predict howmuch intravascular (blood) volume can be safely removed from a patientduring a hemodialysis process. For example, an embodiment might provideinstructions to a human operator of a hemodialysis machine, based onestimates or predictions of the patient's CRI. Additionally and/oralternatively, such embodiments can be used to continuously self-adjustthe ultra-filtration rate of the hemodialysis equipment, therebycompletely avoiding intradialytic hypotension and its associatedmorbidity.

As yet another example, certain embodiments can be used to estimateand/or predict a dehydration state (and/or the amount of dehydration) inan individual (e.g., a trauma patient, an athlete, an elder living athome, etc.) and/or to provide treatment (either by providingrecommendations to treating personnel or by directly controllingappropriate therapeutic equipment). For instance, if an analytical modelindicates a relationship between CRI (and/or any other physiologicalphenomena that can be measured and/or estimated using the techniquesdescribed herein and in the Related Applications) and dehydration state,an embodiment can apply that model, using the techniques describedherein, to estimate a dehydration state of the patient.

Exemplary Systems and Methods

FIG. 1A provides a general overview of a system provided by certainembodiments. The system includes a computer system 100 in communicationwith one or more sensors 105, which are configured to obtainphysiological data from the subject (e.g., animal or human test subjector patient) 110. In one embodiment, the computer system 100 comprises aLenovo THINKPAD X200, 4 GB of RAM with Microsoft WINDOWS 7 operatingsystem and is programmed with software to execute the computationalmethods outlined herein. The computational methods can be implemented inMATLAB 2009b and C++ programming languages. A more general example of acomputer system 100 that can be used in some embodiments is described infurther detail below. Even more generally, however, the computer system100 can be any system of one or more computers that are capable ofperforming the techniques described herein. In a particular embodiment,for example, the computer system 100 is capable of reading values fromthe physiological sensors 105, generating models of physiological statefrom those sensors, and/or employing such models to makeindividual-specific estimations, predictions, or other diagnoses,displaying the results, recommending and/or implementing a therapeutictreatment as a result of the analysis, and/or archiving (learning) theseresults for use in future, model building and predictions.

The sensors 105 can be any of a variety of sensors (including withoutlimitation those described herein) for obtaining physiological data fromthe subject. An exemplary sensor suite might include a Finometer sensorfor obtaining a noninvasive continuous blood pressure waveform, a pulseoximeter sensor, an Analog to Digital Board (National InstrumentsUSB-9215A 16-Bit, 4 channel) for connecting the sensors (either thepulse oximeter and/or the finometer) to the computer system 100. Moregenerally, in an embodiment one or more sensors 105 might obtain, e.g.,using one or more of the techniques described herein, continuousphysiological waveform data, such as continuous blood pressure. Inputfrom the sensors 105 can constitute continuous data signals and/oroutcomes that can be used to generate, and/or can be applied to, apredictive model as described below.

In some cases, the structure might include a therapeutic device 115(also referred to herein as a “physiological assistive device”), whichcan be controlled by the computer system 100 to administer therapeutictreatment, in accordance with the recommendations developed by analysisof a patient's physiological data. In a particular embodiment, thetherapeutic device might comprise hemodialysis equipment (also referredto as a hemodialysis machine), which can be controlled by the computersystem 100 based on the estimated CRI of the patient, as described infurther detail below. Further examples of therapeutic devices in otherembodiments can include a cardiac assist device, a ventilator, anautomatic implantable cardioverter defibrillator (“AICD”), pacemakers,an extracorporeal membrane oxygenation circuit, a positive airwaypressure (“PAP”) device (including without limitation a continuouspositive airway pressure (“cPAP”) device or the like), an anesthesiamachine, an integrated critical care system, a medical robot,intravenous and/or intra-arterial pumps that can provide fluids and/ortherapeutic compounds (e.g., through intravenous injection), intravenousdrips, a rapid infuser, a heating/cooling blanket, and/or the like.

FIG. 1B illustrates in more detail an exemplary sensor device 105, whichcan be used in the system 100 described above. (It should be noted, ofcourse, that the depicted sensor device 105 of FIG. 1B is not intendedto be limiting, and different embodiments can employ any sensor thatcaptures suitable data, including without limitation sensors describedelsewhere in this disclosure and in the Related Applications.) Theillustrated sensor device 105 is designed to be worn on a patient'swrist and therefore can be used both in clinical settings and in thefield (e.g., on any person for whom monitoring might be beneficial, fora variety of reasons, including without limitation assessment of bloodpressure and/or hydration during athletic competition or training, dailyactivities, military training or action, etc.). In one aspect, thesensor device 105 can serve as an integrated hydration monitor, whichcan assess hydration as described herein, display an indication of theassessment, recommend therapeutic action based on the assessment, or thelike, in a form factor that can be worn during athletic events and/ordaily activities.

Hence, the exemplary sensor 105 device (hydration monitor) includes afinger cuff 125 and a wrist unit 130. The finger cuff 125 includes afingertip sensor 135 (in this case, a PPG sensor) that captures databased on physiological conditions of the patient, such as PPG waveformdata. The sensor 135 communicates with an input/output unit 140 of thewrist unit 130 to provide output from the sensor 135 to a processingunit 145 of the wrist unit 130. Such communication can be wired (e.g.,via a standard—such as USB—or proprietary connector on the wrist unit130) and/or wireless (e.g., via Bluetooth, such as Bluetooth Low Energy(“BTLE”), near field connection (“NFC”), WiFi, or any other suitableradio technology).

In different embodiments, the processing unit can have different typesof functionality. For example, in some cases, the processing unit mightsimply act to store and/or organize data prior to transmitting the datathrough the I/O unit 140 to a monitoring computer 100, which mightperform data analysis, control a therapeutic device 115, etc. In othercases, however, the processing unit 145 might act as a specializedcomputer (e.g., with some or all of the components described inconnection with FIG. 5, below and/or some or all of the functionalityascribed to the computer 100 of FIGS. 1A and 1B), such that theprocessing unit can perform data analysis onboard, e.g., to estimateand/or predict a patient's current and/or future blood pressure. Assuch, the wrist unit 105 might include a display, which can display anyoutput described herein, including without limitation estimated and/orpredicted values (e.g., of CRI, blood pressure, hydration status, etc.),data captured by the sensor (e.g., heart rate, pulse ox, etc.), and/orthe like.

In some cases, the wrist unit 130 might include a wrist strap 155 thatallows the unit to be worn on the wrist, similar to a watch. Of course,other options are available to facilitate transportation of the sensordevice 105 with a patent. More generally, the sensor device 105 mightnot include all of the components described above, and/or variouscomponents might be combined and/or reorganized; once again, theembodiment illustrated by FIG. 1B should be considered onlyillustrative, and not limiting, in nature.

FIGS. 2A, 2B, 3A, 3B and 4 illustrate methods and screen displays inaccordance with various embodiments. While the methods of FIGS. 2A, 2B,3A, 3B and 4 are illustrated, for ease of description, as differentmethods, it should be appreciated that the various techniques andprocedures of these methods can be combined in any suitable fashion, andthat, in some embodiments, the methods depicted by FIGS. 2A, 2B, 3A, 3Band 4 can be considered interoperable and/or as portions of a singlemethod. Similarly, while the techniques and procedures are depictedand/or described in a certain order for purposes of illustration, itshould be appreciated that certain procedures may be reordered and/oromitted within the scope of various embodiments. Moreover, while themethods illustrated by FIGS. 2A, 2B, 3A, 3B and 4 can be implemented by(and, in some cases, are described below with respect to) the computersystem 100 of FIG. 1 (or other components of the system, such as thesensor 105 of FIGS. 1A and 1B), these methods may also be implementedusing any suitable hardware implementation. Similarly, while thecomputer system 100 of FIG. 1 (and/or other components of such a system)can operate according to the methods illustrated by FIGS. 2A, 2B, 3A, 3Band 4 (e.g., by executing instructions embodied on a computer readablemedium), the system 100 can also operate according to other modes ofoperation and/or perform other suitable procedures.

Merely by way of example, a method might comprise one or moreprocedures, any or all of which are executed by a computer system.Correspondingly, an embodiment might provide a computer systemconfigured with instructions to perform one or more procedures inaccordance with methods provided by various other embodiments.Similarly, a computer program might comprise a set of instructions thatare executable by a computer system (and/or a processor therein) toperform such operations. In many cases, such software programs areencoded on physical, tangible and/or non-transitory computer readablemedia (such as, to name but a few examples, optical media, magneticmedia, and/or the like).

By way of non-limiting example, various embodiments can comprise amethod for using sensor data to assess the effectiveness of fluidresuscitation of a patient and/or the hydration of a patient. FIG. 2illustrates an exemplary method 200 in accordance with variousembodiments. The method 200 might comprise generating a model, e.g.,with a computer system, against which patient data can be analyzed toestimate and/or predict various physiological states (block 205). In ageneral sense, generating the model can comprise receiving datapertaining to a plurality of more physiological parameters of a testsubject to obtain a plurality of physiological data sets. Such data caninclude PPG waveform data to name one example, and/or any other type ofsensor data including without limitation data captured by other sensorsdescribed herein and in the Related Applications.

Generating a model can further comprise directly measuring one or morephysiological states of the test subject with a reference sensor toobtain a plurality of physiological state measurements. The one or morephysiological states can include, without limitation, states of variousvolumes of blood loss and/or fluid resuscitation, and/or various statesof hydration and/or dehydration. (In other embodiments, different statescan include a state of hypervolemia, a state of euvolemia, and/or astate of cardiovascular collapse (or near-cardiovascular collapse),and/or can include states that have been simulated, e.g., through use ofan LBNP apparatus). Other physiological states that can be used togenerate a model are described elsewhere herein and in the RelatedApplications.

Generating the model can further comprise correlating the physiologicalstate(s) with the measured physiological parameters. There are a varietyof techniques for generating a model in accordance with differentembodiments, using these general functions. One exemplary technique forgenerating a model of a generic physiological state is described belowwith respect to FIG. 4, below, which provides a technique using amachine-learning algorithm to optimize the correlation between measuredphysiological parameters (such as PPG waveform data, to name oneexample) and physical states (e.g., various blood volume states,including states where a known volume of blood loss has occurred and/ora known volume of fluid resuscitation has been administered, variousstates of hydration and/or dehydration, etc.). It should be appreciated,however, that any suitable technique or model may be employed inaccordance with various embodiments.

A number of physiological states can be modeled, and a number ofdifferent conditions can be imposed on test subjects as part of themodel generation. For example, physiological states that can be induced(or monitored when naturally occurring) in test subjects include,without limitation, reduced circulatory system volume, known volume ofblood loss, specified amounts of fluids added to blood volume,dehydration, cardiovascular collapse or near-cardiovascular collapse,euvolemia, hypervolemia, low blood pressure, high blood pressure, normalblood pressure, and/or the like.

Merely by way of example, in one set of embodiments, a number ofphysiological parameters of a plurality of test subjects might bemeasured. In some cases, a subject might undergo varying, measuredlevels of blood loss (either real or simulated) or intravenous fluidaddition. Using the method described below with respect to FIG. 4 (orother, similar techniques, many of which are described in the RelatedApplications), the system can determine which sensor information mosteffectively differentiates between subjects at different bloodloss/addition volume levels.

Additionally and/or alternatively to using direct (e.g., raw) sensordata to build such models, some embodiments might construct a modelbased on data that is derived from sensor data. Merely by way ofexample, one such model might use, as input values, CRI values of testsubjects in different blood loss and/or volume addition conditions.Accordingly, the process of generating a model might first comprisebuilding a model of CRI, and then, from that model, building a model ofhydration effectiveness. (In other cases, a hybrid model might considerboth raw sensor data and CRI data.)

A CRI model can be generated in different ways. For example, in somecases, one or more test subjects might be subjected to LBNP. In anexemplary case, LBNP data is collected from human subjects being exposedto progressively lower levels of LBNP, until hemodynamic decompensation,at which time LBNP is released and the subject recovers. Each level ofLBNP represents an additional amount of blood loss. During these tests,physiological data (including without limitation waveform data, such ascontinuous non-invasive blood pressure data)) can be collected before,during, and/or after the application of the LBNP. As noted above, arelationship (as expressed by Equation 2) can be identified between LBNPand intravascular volume loss, and this relationship can be used toestimate CRI. Hence, LBNP studies form a framework (methodology) for thedevelopment of the hemodynamic parameter referred to herein as CRI andcan be used to generate models of this parameter.

More generally, several different techniques that induce a physiologicalstate of reduced volume in the circulatory system, e.g., to a point ofcardiovascular collapse (hemodynamic decompensation) or to a point nearcardiovascular collapse, can be used to generate such a model. LBNP canbe used to induce this condition, as noted above. In some cases, such asin a study described below, dehydration can be used to induce thiscondition as well. Other techniques are possible as well. Similarly,data collected from a subject in a state of euvolemia, dehydration,hypervolemia, and/or other states might be used to generate a CRI modelin different embodiments.

At block 210, the method 200 comprises monitoring, with one or moresensors, physiological data of a patient. As noted above, a variety ofphysical parameters can be monitored, invasively and/or non-invasively,depending on the nature of the anticipated physiological state of thepatient. In an aspect, monitoring the one or more physical parametersmight comprise receiving, e.g., from a physiological sensor, continuouswaveform data, which can be sampled as necessary. Such data can include,without limitation, plethysmograph waveform data, PPG waveform data(such as that generated by a pulse oximeter), and/or the like.

The method 200 might further comprise analyzing, with a computer system(e.g., a monitoring computer 100 and/or a processing unit 135 of asensor unit, as described above), the physiological data (block 215). Insome cases, the physiological data is analyzed against a pre-existingmodel (which might be generated as described above and which in turn,can be updated based on the analysis, as described in further detailbelow and in the Related Applications).

Merely by way of example, in some cases, sensor data can be analyzeddirectly against a generated model to assess the effectiveness ofhydration (which can include estimating current values, and/orpredicting future values for any or all of HE, V, and/or P_(f), asexpressed above. For example, the sensor data can be compared todetermine similarities with models that estimate and/or predict any ofthese values. Merely by way of example, an input waveform captured by asensor from a patient might be compared with sample waveforms generatedby models for each of these values.

For example, the technique 265 provides one method for deriving anestimate of HE in accordance with some embodiments. It should be notedthat the technique 265 is presented as an example only, and that whilethis technique 265 estimates HE from raw sensor data, similar techniquescan be used to estimate or predict HE, V, and/or P_(f) from raw sensordata, CRI data, and/or a combination of both. For example, one modelmight produce a first estimate of HE from raw sensor data, produce asecond estimate of HE from estimated CRI values, and then combine thoseestimates (in either weighted or unweighted fashion) to produce a hybridHE estimate.

The illustrated technique 265 comprises sampling waveform data (e.g.,any of the data described herein and in the Related Applications,including without limitation arterial waveform data, such as continuousPPG waveforms and/or continuous noninvasive blood pressure waveforms)for a specified period, such as 32 heartbeats (block 270). That sampleis compared with a plurality of waveforms of reference datacorresponding to HE values (block 275), which in this case range from 0to 1 using the scale described above (but alternatively might use anyappropriate scale). These reference waveforms are derived as part of themodel developed using the algorithms described in this and the RelatedApplications, might be the result of experimental data, and/or the like.In effect, these reference waveforms reflect the relationship ƒ from Eq.6, above.

According to the technique 265, the sample might be compared withwaveforms corresponding to a HE=0 (block 275 a), HE=0.5 (block 275 b),and HE=1 (block 275 c), as illustrated. (As illustrated by the ellipseson FIG. 2B, any number of sample waveforms can be used for thecomparison; for example, if there is a nonlinear relationship betweenthe measured sensor data and the HE values, more sample waveforms mightprovide for a better comparison.) From the comparison, a similaritycoefficient is calculated (e.g., using a least squares or similaranalysis) to express the similarity between the sampled waveform andeach of the reference waveforms (block 280). These similaritycoefficients can be normalized (if appropriate) (block 285), and thenormalized coefficients can be summed (block 390) to produce anestimated HE value of the patient.

In other cases, similar techniques can be used to analyze data against amodel based on parameters derived from direct sensor measurements. (Inone aspect, such operations can be iterative in nature, by generatingthe derived parameters—such as CRI, to name one example—by analyzing thesensor data against a first model, and then analyzing the derivedparameters against a second model.

For example, FIG. 3A illustrates a method 300 of calculating a patient'sCRI, which can be used (in some embodiments) as a parameter that can beanalyzed to assess the effectiveness of hydration (including theprobability that fluids are needed and/or the estimated volume of fluidnecessary for effective hydration). The method 300 includes generating amodel of CRI (block 305), monitoring physiological parameters (310) andanalyzing the monitored physical parameters (block 315), usingtechniques such as those described above and in the '483 application,for example.

Based on this analysis, the method 300, in an exemplary embodiment,includes estimating, with the computer system, a compensatory reserve ofthe patient, based on analysis of the physiological data (block 320). Insome cases, the method might further comprise predicting, with thecomputer system, the compensatory reserve of the patient at one or moretime points in the future, based on analysis of the physiological data(block 325). The operations to predict a future value of a parameter canbe similar to those for estimating a current value; in the predictioncontext, however, the applied model might correlate measured data in atest subject with subsequent values of the diagnostic parameter, ratherthan contemporaneous values. It is worth noting, of course, that in someembodiments, the same model can be used to both estimate a current valueand predict future values of a physiological parameter.

The estimated and/or predicted compensatory reserve of the patient canbe based on several factors. Merely by way of example, in some cases,the estimated/predicted compensatory reserve can be based on a fixedtime history of monitoring the physiological data of the patient and/ora dynamic time history of monitoring the physiological data of thepatient. In other cases, the estimated/predicted compensatory reservecan be based on a baseline estimate of the patient's compensatoryreserve established when the patient is euvolemic. In still other cases,the estimate and/or prediction might not be based on a baseline estimateof the patient's compensatory reserve established when the patient iseuvolemic.

Merely by way of example, FIG. 3B illustrates one technique 365 forderiving an estimate of CRI in accordance with some embodiments similarto the technique 265 described above with respect to FIG. 2B forderiving an assessment of hydration effectiveness directly from sensordata (and, in fact, CRI can be derived as described herein, and thatderived value can be used, alone or with raw sensor data, to assess sucheffectiveness). The illustrated technique comprises sampling waveformdata (e.g., any of the data described herein and in the RelatedApplications, including without limitation arterial waveform data, suchas continuous PPG waveforms and/or continuous noninvasive blood pressurewaveforms) for a specified period, such as 32 heartbeats (block 370).That sample is compared with a plurality of waveforms of reference datacorresponding to different CRI values (block 375). (These referencewaveforms might be derived using the algorithms described in the RelatedApplications, might be the result of experimental data, and/or thelike). Merely by way of example, the sample might be compared withwaveforms corresponding to a CRI of 1 (block 375 a), a CRI of 0.5 (block375 b), and a CRI of 0 (block 375 c), as illustrated. From thecomparison, a similarity coefficient is calculated (e.g., using a leastsquares or similar analysis) to express the similarity between thesampled waveform and each of the reference waveforms (block 380). Thesesimilarity coefficients can be normalized (if appropriate) (block 385),and the normalized coefficients can be summed (block 390) to produce anestimated value of the patient's CRI.

Returning to FIG. 3A, the method 300 can comprise estimating and/orpredicting a patient's dehydration state (block 330). The patient'sstate of dehydration can be expressed in a number of ways. For instance,the state of dehydration might be expressed as a normalized value (forexample, with 1.0 corresponding to a fully hydrated state and 0.0corresponding to a state of morbid dehydration). In other cases, thestate of dehydration might be expressed as a missing volume of fluid oras a volume of fluid present in the patient's system, or using any otherappropriate metric.

A number of techniques can be used to model dehydration state. Merely byway of example, as noted above (and described in further detail below),the relationship between a patient's compensatory reserve and level ofdehydration can be modeled. Accordingly, in some embodiments, estimatinga dehydration state of the patient might comprise estimating thecompensatory reserve (e.g., CRI) of the patient, and then, based on thatestimate and the known relationship, estimating the dehydration state.Similarly, a predicted value of compensatory reserve at some point inthe future can be used to derive a predicted dehydration state at thatpoint in the future. Other techniques might use a parameter other thanCRI to model dehydration state.

The method 300 might further comprise normalizing the results of theanalysis (block 335), such as the compensatory reserve, dehydrationstate, and/or probability of bleeding, to name a few examples. Merely byway of example, the estimated/predicted compensatory reserve of thepatient can be normalized relative to a normative normal blood volumevalue corresponding to euvolemia, a normative excess blood volume valuecorresponding to circulatory overload, and a normative minimum bloodvolume value corresponding to cardiovascular collapse. Any values can beselected as the normative values. Merely by way of example, in someembodiments, the normative excess blood volume value is >1, thenormative normal blood volume value is 1, and the normative minimumblood volume value is 0. As an alternative, in other embodiments, thenormative excess blood volume value might be defined as 1, the normativenormal blood volume value might be defined as 0, and the normativeminimum blood volume value at the point of cardiovascular collapse mightbe defined as −1. As can be seen from these examples, differentembodiments might use a number of different scales to normalize CRI andother estimated parameters.

In an aspect, normalizing the data can provide benefits in a clinicalsetting, because it can allow the clinician to quickly make aqualitative judgment of the patient's condition, while interpretation ofthe raw estimates/predictions might require additional analysis. Merelyby way of example, with regard to the estimate of the compensatoryreserve of the patient, that estimate might be normalized relative to anormative normal blood volume value corresponding to euvolemia and anormative minimum blood volume value corresponding to cardiovascularcollapse. Once again, any values can be selected as the normativevalues. For example, if the normative normal blood volume is defined as1, and the normative minimum blood volume value is defined as 0, thenormalized value, falling between 0.0 and 1.0 can quickly apprise aclinician of the patient's location on a continuum between euvolemia andcardiovascular collapse. Similar normalizing procedures can beimplemented for other estimated data (such as probability of bleeding,dehydration, and/or the like).

The method 300 might further comprise displaying data with a displaydevice (block 340). Such data might include an estimate and/orprediction of the compensatory reserve of the patient and/or an estimateand/or prediction of the patient's dehydration state. A variety oftechniques can be used to display such data. Merely by way of example,in some cases, displaying the estimate of the compensatory reserve ofthe patient might comprise displaying the normalized estimate of thecompensatory reserve of the patient. Alternatively and/or additionally,displaying the normalized estimate of the compensatory reserve of thepatient might comprise displaying a graphical plot showing thenormalized excess blood volume value, the normalized normal blood volumevalue, the normalized minimum blood volume value, and the normalizedestimate of the compensatory reserve (e.g., relative to the normalizedexcess blood volume value, the normalized normal blood volume value, thenormalized minimum blood volume value).

In some cases, the method 300 might comprise repeating the operations ofmonitoring physiological data of the patient, analyzing thephysiological data, and estimating (and/or predicting) the compensatoryreserve of the patient, to produce a new estimated (and/or predicted)compensatory reserve of the patient. Thus, displaying the estimate(and/or prediction) of the compensatory reserve of the patient mightcomprises updating a display of the estimate of the compensatory reserveto show the new estimate (and/or prediction) of the compensatoryreserve, in order to display a plot of the estimated compensatoryreserve over time. Hence, the patient's compensatory reserve can berepeatedly estimated and/or predicted on any desired interval (e.g.,after every heartbeat), on demand, etc.

In further embodiments, the method 300 can comprise determining aprobability that the patient is bleeding, and/or displaying, with thedisplay device, an indication of the probability that the patient isbleeding (block 345). For example, some embodiments might generate amodel based on data that removes fluid from the circulatory system (suchas LBNP, dehydration, etc.). Another embodiment might generate a modelbased on fluid removed from a subject voluntarily, e.g., during a blooddonation, based on the known volume (e.g., 500 cc) of the donation.Based on this model, using techniques similar to those described above,a patient's physiological data can be monitored and analyzed to estimatea probability that the patient is bleeding (e.g., internally).

In some cases, the probability that the patient is bleeding can be usedto adjust the patient's estimated CRI. Specifically, give a probabilityof bleeding expressed as Pr_Bleed at a time t, the adjusted value of CRIcan be expressed as:CRI_(Adjusted)(t)=1−((1−CRI(t))×Pr_Bleed(t))  (Eq. 13)

Given this relationship, the estimated CRI can be adjusted to produce amore accurate diagnosis of the patient's condition at a given point intime.

The method 300 might comprise selecting, with the computer system, arecommended treatment option for the patient, and/or displaying, withthe display device, the recommended treatment option (block 355). Therecommended treatment option can be any of a number of treatmentoptions, including without limitation, optimizing hemodynamics of thepatient, a ventilator adjustment, an intravenous fluid adjustment,transfusion of blood or blood products to the patient, infusion ofvolume expanders to the patient, a change in medication administered tothe patient, a change in patient position, and surgical therapy.

In a specific example, the method 300 might comprise controllingoperation of hemodialysis equipment (block 360), based at least in parton the estimate of the patient's compensatory reserve. Merely by way ofexample, a computer system that performs the monitoring and estimatingfunctions might also be configured to adjust an ultra-filtration rate ofthe hemodialysis equipment in response to the estimated CRI values ofthe patient. In other embodiments, the computer system might provideinstructions or suggestions to a human operator of the hemodialysisequipment, such as instructions to manually adjust an ultra-filtrationrate, etc.

In some embodiments, the method 300 might include assessing thetolerance of an individual to blood loss, general volume loss, and/ordehydration (block 365). For example, such embodiments might includeestimating a patient's CRI based on the change in a patient's position(e.g., from lying prone to standing, lying prone to sitting, and/orsitting to standing). Based on changes to the patient's CRI in responseto these maneuvers, the patient's sensitivity to blood loss, volumeloss, and/or dehydration can be measured. In an aspect, this measurementcan be performed using a CRI model generated as described above; thepatient can be monitored using one or more of the sensors describedabove, and the changes in the sensor output when the subject changesposition can be analyzed according to the model (as described above, forexample) to assess the tolerance of the individual to volume loss. Suchmonitoring and/or analysis can be performed in real time.

Returning to FIG. 2, based on the analysis of the data (whether datacollected directly by sensors or derived data, such as CRI, or both)against a model (which might include multiple submodels, such as a modelof HE against raw data and a model of HE against CRI), the method 200can include assessing the effectiveness of hydration of the patient(block 220), based on analysis of the patient's physiological dataagainst the model. As noted above, assessing effectiveness of hydrationcan include estimating or predicting a number of values, such as theestimated effectiveness, HE, of the hydration effort, the volume, V, offluid necessary for effective hydration, the probability, P_(f), thatthe patient needs fluids, and/or the like.

In some cases, the assessment of the effectiveness of hydration will bebased on the analysis of a plurality of measured (or derived) values ofa particular physiological parameter (or plurality of parameters).Hence, in some cases, the analysis of the data might be performed on acontinuous waveform, either during or after measurement of the waveformwith a sensor (or both), and the assessment of the effectiveness can beupdated as hydration efforts continue. Further, the amount of fluidsadded to the patient's blood volume can be measured directly, and thesedirect measurements (at block 235) can be fed back into the model toupdate the model and thereby improve performance of the algorithms inthe model (e.g., by refining the weights given to different parametersin terms of estimative or predictive value). The updated model can thenbe used to continue assessing the treatment (in the instant patientand/or in a future patient), as shown by the broken lines on FIG. 2A.

In some cases, the method 200 comprises displaying data (block 230)indicating the assessment of the effectiveness of hydration. In somecases, the data might be displayed on a display of a sensor device (suchas the device 105 illustrated by FIG. 1B). Alternatively and/oradditionally the data might be displayed on a dedicated machine, such asa compensatory reserve monitor, or on a monitor of a generic computersystem. The data might be displayed alphanumerically, graphically, orboth. FIGS. 6-8, described below, illustrate several possible exemplarydisplays of assessments of hydration effectiveness. There are manydifferent ways that the data can be displayed, and any assessments,estimates or predictions generated by the method 200 can be displayed inany desired way, in accordance with various embodiments.

In certain embodiments, the method 200 can include selecting and/ordisplaying treatment options for the patient (block 235) and/orcontrolling a therapeutic device (block 240) based on the assessment ofthe effectiveness of hydration of the patient. For example, a displaymight indicate to a clinician or the patient him or herself that thepatient is becoming (or has become) dehydrated, that fluid resuscitationtherapy should be initiated, an estimated volume of fluid to drink,infuse, or otherwise consume, a drip rate for an IV drip, a flow ratefor an IV pump or infuser, or the like. Similarly, the system might beconfigured to control operation of a therapeutic device, such asdispensing a fluid to drink from an automated dispenser, activating oradjusting the flow rate of an IV pump or infuser, adjusting the driprate of an IV drip, and/or the like, based on the assessment of theeffectiveness of hydration. As another example, certain embodimentsmight include a water bladder (e.g., a backpack-based hydration pack,such as those available from Camelbak Products LLC) or a water bottle,and the hydration monitor could communicate with and/or controloperation of such a dispensing device (e.g., to cause the device todispense a certain amount of fluid, to cause the device to trigger anaudible alarm, etc.).

Further, in certain embodiments, the method 200 can includefunctionality to help a clinician (or other entity) to monitorhydration, fluid resuscitation and/or blood volume status. For example,in some cases, any measure of effectiveness outside of the normal range(such as a value of P_(f) higher than a certain threshold value, a valueof HE lower than a threshold value, etc.) would set off various alarmconditions, such as an audible alarm, a message to a physician, amessage to the patient, an update written automatically to a patient'schart, etc. Such messaging could be accomplished by electronic mail,text message, etc., and a sensor device or monitoring computer could beconfigured with, e.g., an SMTP client, text messaging client, or thelike to perform such messaging.

In some cases, feedback and/or notifications might be sent to a thirdparty, regardless of whether any alarm condition were triggered. Forexample, a hydration monitor might be configured to send monitoringresults (e.g., any of the assessments, estimates and/or predictionsdescribed herein) to another device or computer, either for personalmonitoring by the patient or for monitoring by another. Examples couldinclude transmitting such alarms or data (e.g., by Bluetooth, NFC, WiFi,etc.) to a wireless phone, wearable device (e.g., smart watch orglasses) or other personal device of the patient, e.g., for inclusion ina health monitoring application. Additionally and/or alternatively, suchinformation could be sent to a specified device or computer (e.g., viaany available IP connection), for example to allow a parent to monitor achild's (or a child to monitor an elderly parent's) hydration remotely,to allow a coach to monitor a player's hydration remotely, and/or toallow a superior officer to monitor a soldier's hydration remotely. Insome cases (e.g., a coach or superior officer), an application mightaggregate results from a plurality of hydration monitors, to allow thesupervisor to view (e.g., in a dashboard-type configuration), hydrationeffectiveness (and/or any other data, such as CRI, blood pressure, etc.)for a group of people. Such a display might employ, for example, aplurality of “fuel gauge” displays, one (or more) for each person in thegroup, allowing the supervisor to quickly ascertain any unusual results(e.g., based on the color of the gauge, etc.).

Similarly, if an alarm condition were met for another physiologicalparameter (such as blood pressure, which can be estimated as describedin the '171 application, for example), that alarm could trigger anassessment of hydration effectiveness via this the method 200, todetermine whether the first alarm condition has merit or not. If not,perhaps there could be an automated silencing of the original alarmcondition, since all is well at present. More generally, the assessmenttechniques could be added to an ecosystem of monitoring algorithms(including without limitation those described in the RelatedApplications), which would inform one another or work in combination, toinform one another about how to maintain optimal physiologicalstability.

FIG. 4 illustrates a method 400 of employing such a self-learningpredictive model (or machine learning) technique, according to someembodiments. In particular, the method 400 can be used to correlatephysiological data received from a subject sensor with a measuredphysiological state. More specifically, with regard to variousembodiments, the method 400 can be used to generate a model forassessing, predicting and/or estimating various physiologicalparameters, such as blood loss volume, effectiveness of hydration orfluid resuscitation efforts, estimated and/or predicted blood pressure,CRI, the probability that a patient is bleeding, a patient's dehydrationstate, and/or the like, from one or more of a number of differentphysiological parameters, including without limitation those describedabove and in the Related Applications.

The method 400 begins at block 405 by collecting raw data measurementsthat may be used to derive a set of D data signals s₁, . . . , s_(D) asindicated at block 410 (each of the data signals s being, in aparticular case, input from one or many different physiologicalsensors). Embodiments are not constrained by the type of measurementsthat are made at block 405 and may generally operate on any data set.For example, data signals can be retrieved from a computer memory and/orcan be provided from a sensor or other input device. As a specificexample, the data signals might correspond to the output of the sensorsdescribed above (which measure the types of waveform data describedabove, such as continuous, non-invasive PPG data and/or blood pressurewaveform data).

A set of K current or future outcomes {right arrow over (o)}=(o_(r), . .. , o_(K)) is hypothesized at block 415 (the outcomes o being, in thiscase, past and/or future physiological states, such as probability thatfluids are needed, volume of fluid needed for effective hydration orfluid resuscitation, HE, CRI, dehydration state, probability ofbleeding, etc.). The method autonomously generates a predictive model Mthat relates the derived data signals {right arrow over (s)} with theoutcomes {right arrow over (o)}. As used herein, “autonomous,” means“without human intervention.”

As indicated at block 420, this is achieved by identifying the mostpredictive set of signals S_(k), where S_(k) contains at least some (andperhaps all) of the derived signals s₁, . . . , s_(D) for each outcomeo_(k), where k∈{1, . . . , K}. A probabilistic predictive modelô_(k)=M_(k) (S_(k)) is learned at block 425, where ô_(k) is theprediction of outcome o_(k) derived from the model Mk that uses asinputs values obtained from the set of signals S_(k), for all k∈{1, . .. , K}. The method 400 can learn the predictive modelsô_(k)=M_(k)(S_(k)) incrementally (block 430) from data that containsexample values of signals s₁, . . . , s_(D) and the correspondingoutcomes o₁, . . . , o_(K). As the data become available, the method 400loops so that the data are added incrementally to the model for the sameor different sets of signals S_(k), for all k∈{1, . . . , K}.

While the description above outlines the general characteristics of themethods, additional features are noted. A linear model framework may beused to identify predictive variables for each new increment of data. Ina specific embodiment, given a finite set of data of signals andoutcomes {({right arrow over (s)}₁, {right arrow over (o)}₁), ({rightarrow over (s)}₂, {right arrow over (o)}₂), . . . }, a linear model maybe constructed that has the form, for all k∈{1, . . . , K},{right arrow over (o)} _(k)=ƒ_(k)(a _(o)+Σ_(i=1) ^(d) a _(i) s_(i))  (Eq. 14)where ƒ_(k) is any mapping from one input to one output, and a₀, a₁, . .. , a_(d) are the linear model coefficients. The framework used toderive the linear model coefficients may estimate which signals s, s₁, .. . , s_(d) are not predictive and accordingly sets the correspondingcoefficients a₀, a₁, . . . , a_(d) to zero. Using only the predictivevariables, the model builds a predictive density model of the data,{({right arrow over (s)}₁, {right arrow over (o)}₁), ({right arrow over(s)}₂, {right arrow over (o)}₂), . . . }. For each new increment ofdata, a new predictive density models can be constructed.

In some embodiments, a prediction system can be implemented that canpredict future results from previously analyzed data using a predictivemodel and/or modify the predictive model when data does not fit thepredictive model. In some embodiments, the prediction system can makepredictions and/or to adapt the predictive model in real-time. Moreover,in some embodiments, a prediction system can use large data sets notonly to create the predictive model, but also predict future results aswell as adapt the predictive model.

In some embodiments, a self-learning, prediction device can include adata input, a processor and an output. Memory can include applicationsoftware that when executed can direct the processor to make aprediction from input data based on a predictive model. Any type ofpredictive model can be used that operates on any type of data. In someembodiments, the predictive model can be implemented for a specific typeof data. In some embodiments, when data is received the predictive modelcan determine whether it understands the data according to thepredictive model. If the data is understood, a prediction is made andthe appropriate output provided based on the predictive model. If thedata is not understood when received, then the data can be added to thepredictive model to modify the model. In some embodiments, the devicecan wait to determine the result of the specified data and can thenmodify the predictive model accordingly. In some embodiments, if thedata is understood by the predictive model and the output generatedusing the predictive model is not accurate, then the data and theoutcome can be used to modify the predictive model. In some embodiments,modification of the predictive model can occur in real-time.

Particular embodiments can employ the tools and techniques described inthe Related Applications in accordance with the methodology describedherein perform the functions of a cardiac reserve monitor, awrist-wearable sensor device, and/or a monitoring computer, as describedherein (the functionality of any or all of which can be combined in asingle, integrated device, in some embodiments). These functionsinclude, but are not limited to assessing fluid resuscitation of apatient, assessing hydration of a patient, monitoring, estimating and/orpredicting a subject's (including without limitation, a patient's)current or future blood pressure and/or compensatory reserve, estimatingand/or determining the probability that a patient is bleeding (e.g.,internally) and/or has been bleeding, recommending treatment options forsuch conditions, and/or the like. Such tools and techniques include, inparticular, the systems (e.g., computer systems, sensors, therapeuticdevices, etc.) described in the Related Applications, the methods (e.g.,the analytical methods for generating and/or employing analyticalmodels, the diagnostic methods, etc.), and the software programsdescribed herein and in the Related Applications, which are incorporatedherein by reference.

Hence, FIG. 5 provides a schematic illustration of one embodiment of acomputer system 500 that can perform the methods provided by variousother embodiments, as described herein, and/or can function as amonitoring computer, CRI monitor, processing unit of sensor device, etc.It should be noted that FIG. 5 is meant only to provide a generalizedillustration of various components, of which one or more (or none) ofeach may be utilized as appropriate. FIG. 5, therefore, broadlyillustrates how individual system elements may be implemented in arelatively separated or relatively more integrated manner.

The computer system 500 is shown comprising hardware elements that canbe electrically coupled via a bus 505 (or may otherwise be incommunication, as appropriate). The hardware elements may include one ormore processors 510, including without limitation one or moregeneral-purpose processors and/or one or more special-purpose processors(such as digital signal processing chips, graphics accelerationprocessors, and/or the like); one or more input devices 515, which caninclude without limitation a mouse, a keyboard and/or the like; and oneor more output devices 520, which can include without limitation adisplay device, a printer and/or the like.

The computer system 500 may further include (and/or be in communicationwith) one or more storage devices 525, which can comprise, withoutlimitation, local and/or network accessible storage, and/or can include,without limitation, a disk drive, a drive array, an optical storagedevice, solid-state storage device such as a random access memory(“RAM”) and/or a read-only memory (“ROM”), which can be programmable,flash-updateable and/or the like. Such storage devices may be configuredto implement any appropriate data stores, including without limitation,various file systems, database structures, and/or the like.

The computer system 500 might also include a communications subsystem530, which can include without limitation a modem, a network card(wireless or wired), an infra-red communication device, a wirelesscommunication device and/or chipset (such as a Bluetooth™ device, an802.11 device, a WiFi device, a WiMax device, a WWAN device, cellularcommunication facilities, etc.), and/or the like. The communicationssubsystem 530 may permit data to be exchanged with a network (such asthe network described below, to name one example), with other computersystems, and/or with any other devices described herein. In manyembodiments, the computer system 500 will further comprise a workingmemory 535, which can include a RAM or ROM device, as described above.

The computer system 500 also may comprise software elements, shown asbeing currently located within the working memory 535, including anoperating system 540, device drivers, executable libraries, and/or othercode, such as one or more application programs 545, which may comprisecomputer programs provided by various embodiments, and/or may bedesigned to implement methods, and/or configure systems, provided byother embodiments, as described herein. Merely by way of example, one ormore procedures described with respect to the method(s) discussed abovemight be implemented as code and/or instructions executable by acomputer (and/or a processor within a computer); in an aspect, then,such code and/or instructions can be used to configure and/or adapt ageneral purpose computer (or other device) to perform one or moreoperations in accordance with the described methods.

A set of these instructions and/or code might be encoded and/or storedon a non-transitory computer readable storage medium, such as thestorage device(s) 525 described above. In some cases, the storage mediummight be incorporated within a computer system, such as the system 500.In other embodiments, the storage medium might be separate from acomputer system (i.e., a removable medium, such as a compact disc,etc.), and/or provided in an installation package, such that the storagemedium can be used to program, configure and/or adapt a general purposecomputer with the instructions/code stored thereon. These instructionsmight take the form of executable code, which is executable by thecomputer system 500 and/or might take the form of source and/orinstallable code, which, upon compilation and/or installation on thecomputer system 500 (e.g., using any of a variety of generally availablecompilers, installation programs, compression/decompression utilities,etc.) then takes the form of executable code.

It will be apparent to those skilled in the art that substantialvariations may be made in accordance with specific requirements. Forexample, customized hardware (such as programmable logic controllers,field-programmable gate arrays, application-specific integratedcircuits, and/or the like) might also be used, and/or particularelements might be implemented in hardware, software (including portablesoftware, such as applets, etc.), or both. Further, connection to othercomputing devices such as network input/output devices may be employed.

As mentioned above, in one aspect, some embodiments may employ acomputer system (such as the computer system 500) to perform methods inaccordance with various embodiments of the invention. According to a setof embodiments, some or all of the procedures of such methods areperformed by the computer system 500 in response to processor 510executing one or more sequences of one or more instructions (which mightbe incorporated into the operating system 540 and/or other code, such asan application program 545) contained in the working memory 535. Suchinstructions may be read into the working memory 535 from anothercomputer readable medium, such as one or more of the storage device(s)525. Merely by way of example, execution of the sequences ofinstructions contained in the working memory 535 might cause theprocessor(s) 510 to perform one or more procedures of the methodsdescribed herein.

The terms “machine readable medium” and “computer readable medium,” asused herein, refer to any medium that participates in providing datathat causes a machine to operation in a specific fashion. In anembodiment implemented using the computer system 500, various computerreadable media might be involved in providing instructions/code toprocessor(s) 510 for execution and/or might be used to store and/orcarry such instructions/code (e.g., as signals). In manyimplementations, a computer readable medium is a non-transitory,physical and/or tangible storage medium. Such a medium may take manyforms, including but not limited to, non-volatile media, volatile media,and transmission media. Non-volatile media includes, for example,optical and/or magnetic disks, such as the storage device(s) 525.Volatile media includes, without limitation, dynamic memory, such as theworking memory 535. Transmission media includes, without limitation,coaxial cables, copper wire and fiber optics, including the wires thatcomprise the bus 505, as well as the various components of thecommunication subsystem 530 (and/or the media by which thecommunications subsystem 530 provides communication with other devices).Hence, transmission media can also take the form of waves (includingwithout limitation radio, acoustic and/or light waves, such as thosegenerated during radio-wave and infra-red data communications).

Common forms of physical and/or tangible computer readable mediainclude, for example, a floppy disk, a flexible disk, a hard disk,magnetic tape, or any other magnetic medium, a CD-ROM, any other opticalmedium, a RAM, ROM, a PROM, and EPROM, a FLASH-EPROM, any other memorychip or cartridge, a carrier wave as described hereinafter, or any othermedium from which a computer can read instructions and/or code.

Various forms of computer readable media may be involved in carrying oneor more sequences of one or more instructions to the processor(s) 510for execution. Merely by way of example, the instructions may initiallybe carried on a magnetic disk and/or optical disc of a remote computer.A remote computer might load the instructions into its dynamic memoryand send the instructions as signals over a transmission medium to bereceived and/or executed by the computer system 500. These signals,which might be in the form of electromagnetic signals, acoustic signals,optical signals and/or the like, are all examples of carrier waves onwhich instructions can be encoded, in accordance with variousembodiments of the invention.

The communications subsystem 530 (and/or components thereof) generallywill receive the signals, and the bus 505 then might carry the signals(and/or the data, instructions, etc. carried by the signals) to theworking memory 535, from which the processor(s) 505 retrieves andexecutes the instructions. The instructions received by the workingmemory 535 may optionally be stored on a storage device 525 eitherbefore or after execution by the processor(s) 510.

FIGS. 6-8 illustrate exemplary screen captures from a display device ofa compensatory reserve monitor, showing various features that can beprovided by one or more embodiments. Similar screens could be shown byother monitoring devices, such as a display of a wrist-wearable sensordevice, a display of a monitoring computer, and/or the like. While FIGS.6-8 use HE as an example condition for illustrative purposes, otherembodiments might also display values for the volume, V, the volume offluid necessary for effective hydration, or the probability, P_(f), thatthe patient needs fluid (including additional fluid, if hydrationefforts already are is underway).

FIG. 6 illustrates an exemplary display 600 of a compensatory reservemonitor implementation where a normalized hydration effectiveness (“HE”)of “1” implies that the hydration efforts have are completely effective,and “0” implies that the hydration efforts are completely ineffective.Values in between “0” and “1” imply a continuum of effectiveness.

FIG. 7A illustrates four screen captures 700 of a display of acompensatory reserve monitor implementation that displays HE as a “fuelgauge” type bar graph for a person undergoing central volume blood lossand subsequent hydration efforts. While FIG. 6 illustrates a trace of HEover time, the bar graphs of FIG. 7A provide snapshots of HE at the timeof each screen capture. (In the illustrated implementation, the bargraphs are continuously and/or periodically updates, such that each bargraph could correspond to a particular position on the X-axis of FIG.6.)

A variety of additional features are possible. Merely by way of exampleFIG. 7B illustrates similar “fuel gauge” type displays, but the displays705 of FIG. 7B feature bars of different colors—for example, green(illustrated by diagonal cross-hatching), yellow (illustrated by achecked pattern) and red illustrated by gray shading) corresponding todifferent levels of HE, along with arrows 710 indicating trending in theHE values (e.g., rising, declining, or remaining stable).

In some embodiments, such a “fuel gauge” display (or other indicator ofHE and/or different physiological parameters) can be incorporated in amore comprehensive user interface. Merely by way of example, FIG. 8illustrates an exemplary display 800 of a monitoring system. The display800 includes a graphical, color-coded “fuel gauge” type display 805 ofthe current estimated HE (similar to the displays illustrated by FIG.8B), along with a historical display 810 of recent CRI estimates; inthis example, each bar on the historical display 810 might correspond toan estimate performed every minute, but different estimate frequenciesare possible, and in some embodiments, the operator can be given theoption to specify a different frequency. In the illustrated embodiment,the display 800 also includes numerical display 815 of the current HE aswell as a trend indicator 820 (similar to that indicated above).

In particular embodiments, the display 800 can include additionalinformation (and, in some cases, the types of information displayedand/or the type of display can be configured by the operator). Forinstance, the exemplary display 800 includes an indicator 825 of thepatient's current heart rate and an indicator 830 of the patient's bloodoxygen saturation level (SpO2). The exemplary display 800 also includesan indicator of the estimated volume, V, necessary for effectivehydration, as well as an numerical indicator 840, a trend indicator 845,and a similar color coded “fuel gauge” display 850 of the current CRIOther monitored parameters might be displayed as well, such as an ECGtracing, blood pressure, probability of bleeding estimates, and/or thelike.

CONCLUSION

This document discloses novel tools and techniques for estimatinghydration effectiveness, fluid resuscitation effectiveness, compensatoryreserve and similar physiological states. While certain features andaspects have been described with respect to exemplary embodiments, oneskilled in the art will recognize that numerous modifications arepossible. For example, the methods and processes described herein may beimplemented using hardware components, software components, and/or anycombination thereof. Further, while various methods and processesdescribed herein may be described with respect to particular structuraland/or functional components for ease of description, methods providedby various embodiments are not limited to any particular structuraland/or functional architecture but instead can be implemented on anysuitable hardware, firmware and/or software configuration. Similarly,while certain functionality is ascribed to certain system components,unless the context dictates otherwise, this functionality can bedistributed among various other system components in accordance with theseveral embodiments.

Moreover, while the procedures of the methods and processes describedherein are described in a particular order for ease of description,unless the context dictates otherwise, various procedures may bereordered, added, and/or omitted in accordance with various embodiments.Moreover, the procedures described with respect to one method or processmay be incorporated within other described methods or processes;likewise, system components described according to a particularstructural architecture and/or with respect to one system may beorganized in alternative structural architectures and/or incorporatedwithin other described systems. Hence, while various embodiments aredescribed with—or without—certain features for ease of description andto illustrate exemplary aspects of those embodiments, the variouscomponents and/or features described herein with respect to a particularembodiment can be substituted, added and/or subtracted from among otherdescribed embodiments, unless the context dictates otherwise.Consequently, although several exemplary embodiments are describedabove, it will be appreciated that the invention is intended to coverall modifications and equivalents within the scope of the followingclaims.

What is claimed is:
 1. A hydration monitor, comprising: one or moresensors to obtain physiological data from a patient, wherein thephysiological data is cardiovascular data of the patient; and a computersystem in communication with the one or more sensors, the computersystem comprising: one or more processors; and a computer readablemedium in communication with the one or more processors, the computerreadable medium having encoded thereon a set of instructions executableby the computer system to cause the computer system to: receive thephysiological data from the one or more sensors; estimate one or morecompensatory reserve index (“CRT”) values by comparing the physiologicaldata to a pre-existing model, the pre-existing model comprising aplurality of waveforms of reference data, each waveform of the pluralityof waveforms corresponding to a respective CRT value determined by thefollowing formula: ${{CRI}(t)} = {1 - \frac{{BLV}(t)}{{BLV}_{HDD}}}$where CRI(t) is the compensatory reserve at time t, BLV(t) is anintravascular volume loss of the patient at time t, and BLV_(HDD) is anintravascular volume loss at a point of hemodynamic decompensation ofthe patient, wherein the physiological data includes waveform data ofthe patient, wherein waveform data of the patient includes one or morepatient waveforms, wherein comparing the physiological data against thepre-existing model comprises comparing the waveform data of the patientagainst the plurality of waveforms of reference data, and determining asimilarity between a respective patient waveform of the one or morepatient waveforms and each of one or more waveforms of the plurality ofwaveforms of reference data individually, and wherein estimating the oneor more CRI values of the patient is based, at least in part, onrespective similarities of the respective patient waveform to each ofthe one or more waveforms of the plurality of waveforms of referencedata individually; assess effectiveness of hydration of the patient,wherein the effectiveness of hydration is a numeric value based, atleast in part, on the one or more CRI values, wherein the numeric valueis related to a respective CRI value of the one or more CRI values by ahydration model relating the hydration effectiveness value to therespective CRI value, wherein the hydration model is generatedempirically based on a test population; display, on a display device, anassessment of the effectiveness of hydration of the patient; determine,based on the assessment of the effectiveness of hydration of thepatient, whether the patient requires further hydration; andautomatically control operation of a therapeutic device based on theassessment of the effectiveness of hydration of the patient and based ona determination that the patient requires further hydration, whereinautomatically controlling operation of the therapeutic device comprisesat least one of controlling dispensation of a drink from a drinkdispenser, controlling an alarm on a drink dispenser, or controlling adrip rate of an intravenous drip.
 2. The hydration monitor of claim 1,further comprising the therapeutic device.
 3. The hydration monitor ofclaim 1, wherein the one or more sensors comprise a finger cuffcomprising a fingertip photoplethysmograph and wherein the computersystem comprises a wrist unit in communication with the fingertipphotoplethysmograph, the wrist unit further comprising a wrist strap. 4.A method, comprising: monitoring, with one or more sensors,physiological data of a patient, wherein the physiological data iscardiovascular data of the patient; estimating one or more compensatoryreserve index (“CRT”) values by comparing the physiological data to apre-existing model, the pre-existing model comprising a plurality ofwaveforms of reference data, each waveform of the plurality of waveformscorresponding to a respective CRT value determined by the followingformula: ${{CRI}(t)} = {1 - \frac{{BLV}(t)}{{BLV}_{HDD}}}$ where CRI(t)is the compensatory reserve at time t, BLV(t) is an intravascular volumeloss of the patient at time t, and BLV_(HDD) is an intravascular volumeloss at a point of hemodynamic decompensation of the patient, whereinthe physiological data includes waveform data of the patient, whereinwaveform data of the patient includes one or more patient waveforms,wherein comparing the physiological data against the pre-existing modelcomprises comparing the waveform data of the patient against theplurality of waveforms of reference data, and determining a similaritybetween a respective patient waveform of the one or more patientwaveforms and each of one or more waveforms of the plurality ofwaveforms of reference data individually, and wherein estimating the oneor more CRI values of the patient is based, at least in part, onrespective similarities of the respective patient waveform to each ofthe one or more waveforms of the plurality of waveforms of referencedata individually; assessing effectiveness of hydration of the patient,wherein the effectiveness of hydration is a numeric value based, atleast in part, on the one or more CRI values, wherein the numeric valueis related to a respective CRI value of the one or more CRI values by ahydration model relating the hydration effectiveness value to therespective CRI value, wherein the hydration model is generatedempirically based on a test population; displaying, on a display device,an assessment of the effectiveness of hydration of the patient;determining, based on the assessment of the effectiveness of hydrationof the patient, whether the patient requires further hydration; andautomatically controlling operation of a therapeutic device based on theassessment of the effectiveness of hydration of the patient and based ona determination that the patient requires further hydration, whereinautomatically controlling operation of the therapeutic device comprisesat least one of controlling dispensation of a drink from a drinkdispenser, controlling an alarm on a drink dispenser, or controlling adrip rate of an intravenous drip.
 5. The method of claim 4, whereinassessing effectiveness of hydration of the patient comprises estimatingthe effectiveness of hydration of the patient at a current time.
 6. Themethod of claim 4, wherein assessing effectiveness of hydration of thepatient comprises predicting the effectiveness of hydration of thepatient at a future time.
 7. The method of claim 4, wherein assessingeffectiveness of hydration of the patient comprises estimating an amountof fluid needed for effective hydration of the patient.
 8. The method ofclaim 4, wherein determining, based on the assessment of theeffectiveness of hydration of the patient, whether the patient requiresfurther hydration comprises estimating a probability that the patientrequires fluids.
 9. The method of claim 4, wherein the physiologicaldata includes waveform data and the pre-existing model includes one ormore sample waveforms, wherein estimating each of the one or more CRIvalues of the patient comprises comparing the waveform data with the oneor more sample waveforms generated by exposing one or more test subjectsto a state of hemodynamic decompensation or near hemodynamicdecompensation, or a series of states progressing towards hemodynamicdecompensation, and monitoring physiological data of the test subjects.10. The method of claim 4, wherein determining the similarity betweenthe respective patient waveform and each of the one or more waveforms ofthe plurality of waveforms of reference data individually furthercomprises: producing one or more similarity coefficients, eachsimilarity coefficient of the one or more similarity coefficientsexpressing a respective similarity between the respective patientwaveform and each waveform of the one or more waveforms of the of theplurality of waveforms of reference data individually; whereinestimating the one or more CRI values of the patient further comprises:normalizing the one or more similarity coefficients of the one or morewaveforms of the plurality of waveforms of reference data; summing eachrespective CRI value, corresponding to a respective individual waveformof the one or more waveforms of the plurality of waveforms of referencedata, weighted by the normalized similarity coefficient corresponding tothe respective individual waveform of the one or more waveforms of theplurality of waveforms of reference data, for each of the one or morewaveforms of the plurality of waveforms of reference data; anddetermining, for the respective patient waveform, an estimated CRI valuefor the patient based on the sum of each of the CRI values as weightedby the the normalized similarity coefficients.
 11. The method of claim4, wherein assessing effectiveness of hydration of a patient comprisesassessing effectiveness of hydration of a patient based on a fixed timehistory of monitoring the physiological data of the patient.
 12. Themethod of claim 4, wherein assessing effectiveness of hydration of apatient comprises assessing effectiveness of hydration of a patientbased on a dynamic time history of monitoring the physiological data ofthe patient.
 13. The method of claim 4, wherein at least one of the oneor more sensors is selected from the group consisting of a bloodpressure sensor, an intracranial pressure monitor, a central venouspressure monitoring catheter, an arterial catheter, anelectroencephalograph, a cardiac monitor, a transcranial Doppler sensor,a transthoracic impedance plethysmograph, a pulse oximeter, a nearinfrared spectrometer, a ventilator, an accelerometer, and an electronicstethoscope.
 14. The method of claim 4, wherein the physiological datacomprises blood pressure waveform data.
 15. The method of claim 4,wherein the physiological data comprises plethysmograph waveform data.16. The method of claim 4, wherein the physiological data comprisesphotoplethysmograph (PPG) waveform data.
 17. The method of claim 4,further comprising: generating the pre-existing model.
 18. The method ofclaim 17, wherein the plurality of waveforms of reference data isgenerated by inducing one or more test subjects to enter one or morephysiological states, and obtaining physiological data from one or moretest subjects while the one or more test subjects is in a respectivephysiological state of the one or more physiological states.
 19. Themethod of claim 18, further comprising correlating the physiologicaldata of the test subject to the respective physiological state, whereincorrelating the physiological data of the one or more test subjects withthe physiological state of the test subject comprises: identifying amost predictive set of signals S_(k) out of a set of signals s₁, s₂, . .. , s_(D) for each of one or more outcomes o_(k), wherein themost-predictive set of signals S_(k) corresponds to a first data setrepresenting a first physiological parameter of the physiological dataof the one or more test subjects, and wherein each of the one or moreoutcomes o_(k) represents a physiological state measurement of the oneor more physiological states respectively; autonomously learning a setof probabilistic predictive models ô_(k)=M_(K) (S_(K)), where ô_(k) is aprediction of outcome o_(k) derived from a model M_(k) that uses asinputs values obtained from the set of signals S_(k); and repeating theoperation of autonomously learning incrementally from data that containsexamples of values of signals s₁, s₂, . . . , s_(D) and correspondingoutcomes o₁, o₂, . . . , o_(K).
 20. An apparatus, comprising: anon-transitory computer readable medium having encoded thereon a set ofinstructions executable by one or more computers to cause the apparatusto: receive physiological data from one or more sensors; analyze thephysiological data against a pre-existing model; estimate one or morecompensatory reserve index (“CRI”) values by comparing the physiologicaldata to the pre-existing model, the pre-existing model comprising aplurality of waveforms of reference data, each waveform of the pluralityof waveforms corresponding to a respective CRI value determined by thefollowing formula: ${{CRI}(t)} = {1 - \frac{{BLV}(t)}{{BLV}_{HDD}}}$where CRI(t) is the compensatory reserve at time t, BLV(t) is anintravascular volume loss of a test subject at time t, and BLV_(HDD) isan intravascular volume loss at a point of hemodynamic decompensation ofthe test subject, wherein the physiological data includes waveform dataof the patient, wherein waveform data of the patient includes one ormore patient waveforms, wherein comparing the physiological data againstthe pre-existing model comprises comparing the waveform data of thepatient against the plurality of waveforms of reference data, anddetermining a similarity between a respective patient waveform of theone or more patient waveforms and each of one or more waveforms of theplurality of waveforms of reference data individually, and whereinestimating the one or more CRI values of the patient is based, at leastin part, on respective similarities of the respective patient waveformto each of the one or more waveforms of the plurality of waveforms ofreference data individually; assess effectiveness of hydration of thepatient based at least in part on the one or more CRI values, whereinthe effectiveness of hydration is a numeric value that is related to arespective CRI value of the one or more CRI values by a hydration modelrelating the hydration effectiveness value to the respective CRI value,wherein the hydration model relating the hydration effectiveness valueto the respective CRI value is generated empirically based on a testpopulation; display, on a display device, an assessment of theeffectiveness of hydration of the patient; determine, based on theassessment of the effectiveness of hydration of the patient, whether thepatient requires further hydration; and automatically control operationof a therapeutic device based on the assessment of the effectiveness ofhydration of the patient and based on a determination that the patientrequires further hydration, wherein automatically controlling operationof the therapeutic device comprises at least one of controllingdispensation of a drink from a drink dispenser, controlling an alarm ona drink dispenser, or controlling a drip rate of an intravenous drip.21. The method of claim 4, wherein assessing effectiveness of hydrationof the patient comprises estimating the effectiveness of hydration ofthe patient at a current time and predicting the effectiveness ofhydration of the patient at a future time, the method furthercomprising: estimating a probability that the patient requires fluids;estimating a first amount of fluid needed for effective hydration of thepatient at the current time; and predicting a second amount of fluidneeded for effective hydration of the patient at the future time. 22.The method of claim 4, wherein the operations of monitoringphysiological data of the patient, analyzing the physiological dataagainst the pre-existing model, assessing effectiveness of hydration ofthe patient based at least in part on the one or more CRI values,displaying the assessment of the effectiveness of hydration of thepatient, determining whether the patient requires further hydration, andautomatically controlling operation of the therapeutic device based onthe assessment of the effectiveness of hydration of the patient andbased on a determination that the patient requires further hydration areautomatically repeated iteratively.